In this work, we investigate practical approaches of available degradation models and their usage in photovoltaic (PV) modules and systems. On the one hand, degradation prediction of models is described for the calculation of degradation at system level where the degradation mode is unknown and hence the physics cannot be included by the use of analytical models. Several statistical models are thus described and applied for the calculation of the performance loss using as case study two PV systems, installed in Bolzano/Italy. Namely, simple linear regression (SLR), classical seasonal-decomposition, seasonal-and trend-decomposition using Loess (STL), Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA) are discussed. The performance loss results show that SLR produces results with highest uncertainties. In comparison, STL and ARIMA perform with the highest accuracy, whereby STL is favored because of its easier implementation. On the other hand, if monitoring data at PV module level are available in controlled conditions, analytical models can be applied. Several analytical models depending on different degradations modes are thus discussed. A comparison study is carried out for models proposed for corrosion. Although the results of the models in question agree in explanation of experimental observations, a big difference in degradation prediction was observed. Finally, a model proposed for potential induced degradation was applied to simulate the degradation of PV systems maximum power in three climatic zones: alpine (Zugspitze, Germany), maritime (Gran Canaria, Spain), and arid (Negev, Israel). As expected, a more severe degradation is predicted for arid climates.

No abstract

This article presents an initial performance analysis of a database of photovoltaic (PV) system performance time series collected within the European funded COST Action PEARL PV. The database contains monitoring data of over 8400 PV systems with accompanying metadata. The PV plants are small residential systems, primarily installed in Europe, with a high density in Belgium. In this initial study, the annual average performance ratio, the annual energy yield, and the performance loss rate of the systems are determined and evaluated. The systems have an average lifetime of 30.5 months. The annual mean performance ratio across all systems is 76.7% and the average yield is 954.9 kWh/kWp per year. The performance loss rate is calculated using three different statistical approaches and one irradiance data source. Average performance losses between −0.74%/year and −0.86%/year are calculated depending on the used approach. Furthermore, certain weather-dependent correlations are detected, such as decreasing performance ratio and increasing yield values with increasing irradiation. This study is a stepping-stone for further populating the present database, lessons learnt for handling large amounts of PV performance data, and carrying out performance studies of PV system fleets installed across Europe.

The ever‐growing secondary market of photovoltaic (PV) systems (i.e., the transaction of solar plants ownership) calls for reliable and high‐quality long‐term PV degradation forecasts to mitigate the financial risks. However, when long‐term PV performance degradation forecasts are required after a short time with limited degradation history, the existing physical and data‐driven methods often provide unrealistic degradation scenarios. Therefore, we present a new data‐driven method to forecast PV lifetime after a small performance degradation of only 3%. To achieve an accurate and reliable forecast, the developed method addresses the fundamental challenges that usually affect long‐term degradation evaluation such as data treatment, choosing a good degradation model, and understanding the different degradation patterns. In the paper, we propose and describe an algorithm for degradation trend evaluation, a new concept of multiple “time‐ and degradation pattern‐dependent” degradation factors. The proposed method has been calibrated and validated using different PV modules and systems data of 5 to 35 years of field exposure. The model has been benchmarked against existing statistical models evaluating 11 experimental PV systems with different technologies. The key advantage of our model over statistical ones is the ability to perform more reliable forecasts with limited degradation history. With an average relative uncertainty of 7.0%, our model is outstanding in consistency for different forecasting time horizons. Moreover, the model is applicable to all PV technologies. The proposed method will aid in making reliable financial decisions and also in adequately planning operation and maintenance activities.

Photovoltaic monitoring data are the primary source for studying photovoltaic plant behavior. In particular, performance loss and remaining-useful-lifetime calculations rely on trustful input data. Furthermore, a regular stream of high quality is the basis for pro-active operation and management activities which ensure a smooth operation of PV plants. The raw data under investigation are electrical measurements and usually meteorological data such as in-plane irradiance and temperature. Usually, performance analyses follow a strict pattern of checking input data quality followed by the application of appropriate filter, choosing a key performance indicator and the application of certain methodologies to receive a final result. In this context, this paper focuses on four main objectives. We present common photovoltaics monitoring data quality issues, provide visual guidelines on how to detect and evaluate these, provide new data imputation approaches, and discuss common filtering approaches. Data imputation techniques for module temperature and irradiance data are discussed and compared to classical approaches. This work is intended to be a soft introduction into PV monitoring data analysis discussing best practices and issues an analyst might face. It was seen that if a sufficient amount of training data is available, multivariate adaptive regression splines yields good results for module temperature imputation while histogram-based gradient boosting regression outperforms classical approaches for in-plane irradiance transposition. Based on tested filtering procedures, it is believed that standards should be developed including relatively low irradiance thresholds together with strict power-irradiance pair filters.

The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high‐quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.

Analyses of performance loss rates in photovoltaic (PV) systems are not yet standardized, and are typically carried out by a linear regression of the evolution of a certain performance metric (e.g., performance ratio, yield, etc.) over time. In this article, we propose a novel methodology of advanced PV system performance loss rate modeling applying a self-regulated multistep algorithm. The developed algorithm automatically detects the number and positions of breakpoints in nonlinear performance time series and divides the performance trend into an adequate number of linear segments. Instead of calculating one linear performance loss rate, given in percentage per year, as is common practice, multiple linear performance loss values are determined, depending on the trend of the time series and subsequently the number of breakpoints. The algorithm is fully automated. We have applied our methodology on data of an experimental PV installation in Bolzano/Italy, which consists of 26 different PV systems. The overall linear performance loss rate of the facility's crystalline silicon systems is between −0.5 and −1.3%/year, whereas the thin-film PV systems experience values between −0.6 and −2.4%/year. Based on our results, the algorithm appears to be stable and accurate. The methodology is to be used as a fast and automated check of PV systems in operation to detect anomalies affecting performance (in an early stage). By building up a large database of detected issues in the field this algorithm will enable us to better understand the performance evolution of different PV system types in varying climates.

The performance loss rate (PLR) is a vital parameter for the time-dependent assessment of photovoltaic (PV) system performance and health state. Although this metric can be calculated in a relatively straightforward manner, it is challenging to achieve accurate and reproducible results with low uncertainty. Furthermore, the temporal evolution of PV system performance is usually nonlinear, but in many cases a linear evaluation is preferred as it simplifies the assessment and it is easier to evaluate. As such, the search for a robust and reproducible calculation methodology providing reliable linear PLR values across different types of systems and conditions has been the focus of many research activities in recent years. In this paper, the determination of PV system PLR using different pipelines and approaches is critically evaluated and recommendations for best practices are given. As nonlinear PLR assessments are fairly new, there is no consent on how to calculate reliable values. Several promising nonlinear approaches have been developed recently and are presented as tools to evaluate the PV system performance in great detail. Furthermore, challenges are discussed with respect to the PLR calculation but also opportunities for differentiating individual performance losses from a generic PLR value having the potential of enabling actionable insights for maintenance.